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ipython.py
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"""IPython Magics
IPython magics to access to Treasure Data. Load the magics first of all:
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
"""
import argparse
import os
import re
import sys
import numpy as np
import pandas as pd
import pytz
import tdclient
from IPython import display, get_ipython
from IPython.core import magic
from . import connect, create_engine, read_td_job, read_td_query
MAGIC_CONTEXT_NAME = "_td_magic"
class MagicContext(object):
def __init__(self):
self.database = None
def connect(self):
return connect()
class MagicTable(object):
def __init__(self, table):
print("INFO: import {0}".format(table.name))
self.table = table
data = [c if len(c) == 3 else [c[0], c[1], ""] for c in table.schema]
self.columns = [c[2] if c[2] else c[0] for c in data]
self.frame = pd.DataFrame(data, columns=["field", "type", "alias"])
def __dir__(self):
return self.columns
def _repr_html_(self):
return self.frame._repr_html_()
def get_td_magic_context():
ipython = get_ipython()
try:
ctx = ipython.ev(MAGIC_CONTEXT_NAME)
except NameError:
ctx = MagicContext()
ipython.push({MAGIC_CONTEXT_NAME: ctx})
return ctx
class TDMagics(magic.Magics):
def __init__(self, shell):
super(TDMagics, self).__init__(shell)
self.context = get_td_magic_context()
@magic.magics_class
class DatabasesMagics(TDMagics):
@magic.line_magic
def td_databases(self, pattern):
"""List databases in the form of pandas.DataFrame.
.. code-block:: python
%td_databases [<database_name_pattern>]
Parameters
----------
``<database_name_pattern>`` : string, optional
List databases matched to a given pattern. If not given, all existing
databases will be listed.
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %td_databases sample
Out[2]:
name count permission created_at updated_at
0 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 348124 administrator 2019-01-23 05:48:11+00:00 2019-01-23 05:48:11+00:00
1 yyyyyyyyy 0 administrator 2017-12-14 07:52:34+00:00 2017-12-14 07:52:34+00:00
2 zzzzzzzzzzzzz 0 administrator 2016-05-25 23:12:06+00:00 2016-05-25 23:12:06+00:00
...
In [3]: %td_databases sample
Out[3]:
name count permission created_at updated_at
0 sampledb 2 administrator 2014-04-11 22:29:38+00:00 2014-04-11 22:29:38+00:00
1 sample_xxxxxxxx 2 administrator 2017-06-02 23:37:41+00:00 2017-06-02 23:37:41+00:00
2 sample_datasets 8812278 query_only 2014-10-04 01:13:11+00:00 2018-03-16 04:59:06+00:00
...
"""
con = self.context.connect()
columns = ["name", "count", "permission", "created_at", "updated_at"]
values = [
[getattr(db, c) for c in columns]
for db in con.list_databases()
if re.search(pattern, db.name)
]
return pd.DataFrame(values, columns=columns)
@magic.magics_class
class TablesMagics(TDMagics):
@magic.line_magic
def td_tables(self, pattern):
"""List tables in databases.
.. code-block:: python
%td_tables [<table_identifier_pattern>]
Parameters
----------
``<table_identifier_pattern>`` : string, optional
List tables matched to a given pattern. Table identifier is
represented as ``database_name.table_name``. If not given, all
existing tables will be listed.
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %td_tables
Out[2]:
db_name name count estimated_storage_size last_log_timestamp created_at
0 xxxxx_demo_aa customer_test 70 1047 2018-02-05 06:20:32+00:00 2018-02-05 06:20:24+00:00
1 xxxxx_demo_aa email_log 0 0 1970-01-01 00:00:00+00:00 2018-02-05 07:19:57+00:00
2 yy_wf topk_similar_items 10598 134208 2018-04-16 09:23:57+00:00 2018-04-16 09:59:48+00:00
...
In [3]: %td_tables sample
Out[3]:
db_name name count estimated_storage_size last_log_timestamp created_at
0 xx_test aaaaaaaa_sample 0 0 1970-01-01 00:00:00+00:00 2015-10-20 17:37:40+00:00
1 sampledb sampletbl 2 843 1970-01-01 00:00:00+00:00 2014-04-11 22:30:08+00:00
2 zzzz_test_db sample_output_tab 4 889 2018-06-06 08:26:20+00:00 2018-06-06 08:27:12+00:00
...
"""
con = self.context.connect()
columns = [
"db_name",
"name",
"count",
"estimated_storage_size",
"last_log_timestamp",
"created_at",
]
values = [
[getattr(t, c) for c in columns]
for db in con.list_databases()
for t in con.list_tables(db.name)
if re.search(pattern, t.identifier)
]
return pd.DataFrame(values, columns=columns)
@magic.magics_class
class JobsMagics(TDMagics):
@magic.line_magic
def td_jobs(self, line):
"""List job activities in an account.
.. code-block:: python
%td_jobs
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %td_jobs
Out[2]:
status job_id type start_at query
0 error 448650806 hive 2019-04-12 05:33:36+00:00 with null_samples as (\\n select\\n id,\\n ...
1 success 448646994 presto 2019-04-12 05:23:29+00:00 -- read_td_query\\n-- set session distributed_j...
2 success 448646986 presto 2019-04-12 05:23:27+00:00 -- read_td_query\\n-- set session distributed_j...
...
"""
con = self.context.connect()
columns = ["status", "job_id", "type", "start_at", "query"]
values = [
[j.status(), j.job_id, j.type, j._start_at, j.query]
for j in con.list_jobs()
]
return pd.DataFrame(values, columns=columns)
@magic.magics_class
class UseMagics(TDMagics):
@magic.line_magic
def td_use(self, line):
"""Use a specific database.
This magic pushes all table names in a specified database into the
current namespace.
.. code-block:: python
%td_use [<database_name>]
Parameters
----------
``<database_name>`` : string
Database name.
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %td_use sample_datasets
INFO: import nasdaq
INFO: import www_access
In [3]: nasdaq # describe table columns in the form of DataFrame
Out[3]: <pytd.pandas_td.ipython.MagicTable at 0x117651908>
"""
con = self.context.connect()
try:
tables = con.list_tables(line)
except tdclient.api.NotFoundError:
sys.stderr.write("ERROR: Database '{0}' not found.".format(line))
return
# update context
self.context.database = line
# push table names
get_ipython().push({t.name: MagicTable(t) for t in tables})
@magic.magics_class
class QueryMagics(TDMagics):
def create_job_parser(self):
parser = argparse.ArgumentParser(
prog="job", description="Line magic to get job result.", add_help=False
)
parser.add_argument("job_id", type=int, help="job ID")
parser.add_argument(
"--pivot", action="store_true", help="run pivot_table against dimensions"
)
parser.add_argument("--plot", action="store_true", help="plot the query result")
parser.add_argument(
"-n",
"--dry-run",
action="store_true",
help="output translated code without running query",
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="verbose output"
)
parser.add_argument("-c", "--connection", help="use specified connection")
parser.add_argument(
"-d",
"--dropna",
action="store_true",
help="drop columns if all values are NA",
)
parser.add_argument("-o", "--out", help="store the result to variable")
parser.add_argument("-O", "--out-file", help="store the result to file")
parser.add_argument(
"-q", "--quiet", action="store_true", help="disable progress output"
)
parser.add_argument("-T", "--timezone", help="set timezone to time index")
return parser
def parse_job_args(self, line):
parser = self.create_job_parser()
args = parser.parse_args(line.split())
# validate timezone
if args.timezone:
pytz.timezone(args.timezone)
# implicit options
if args.plot:
args.pivot = True
return args
def create_query_parser(self, engine_type):
parser = argparse.ArgumentParser(
prog=engine_type, description="Cell magic to run a query.", add_help=False
)
parser.add_argument("database", nargs="?", help="database name")
parser.add_argument(
"--pivot", action="store_true", help="run pivot_table against dimensions"
)
parser.add_argument("--plot", action="store_true", help="plot the query result")
parser.add_argument(
"-n",
"--dry-run",
action="store_true",
help="output translated code without running query",
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="verbose output"
)
parser.add_argument("-c", "--connection", help="use specified connection")
parser.add_argument(
"-d",
"--dropna",
action="store_true",
help="drop columns if all values are NA",
)
parser.add_argument("-o", "--out", help="store the result to variable")
parser.add_argument("-O", "--out-file", help="store the result to file")
parser.add_argument(
"-q", "--quiet", action="store_true", help="disable progress output"
)
parser.add_argument("-T", "--timezone", help="set timezone to time index")
return parser
def parse_query_args(self, engine_type, line):
parser = self.create_query_parser(engine_type)
args = parser.parse_args(line.split())
# validate timezone
if args.timezone:
pytz.timezone(args.timezone)
# implicit options
if args.plot:
args.pivot = True
# context
if args.database is None:
args.database = self.context.database
return args
def push_code(self, code, end="\n"):
self.code_list.append(code + end)
def display_code_block(self):
html = '<pre style="background-color: #ffe;">'
html += "".join(self.code_list)
html += "</pre>\n"
display.display(display.HTML(html))
def build_query(self, cell):
ip = get_ipython()
query = cell.format(**ip.user_ns)
self.push_code("_q = '''")
self.push_code(query)
self.push_code("'''")
return query
def build_engine(self, engine_type, database, args):
ip = get_ipython()
name = "{}:{}".format(engine_type, database)
code_args = [repr(name)]
# connection
if args.connection:
con = ip.ev(args.connection)
code_args.append("con={}".format(args.connection))
else:
con = self.context.connect()
# engine
if args.quiet:
params = {"show_progress": False, "clear_progress": False}
elif args.verbose:
params = {"show_progress": True, "clear_progress": False}
else:
params = {}
code_args += ["{}={}".format(k, v) for k, v in params.items()]
self.push_code(
"_e = pytd.pandas_td.create_engine({})".format(", ".join(code_args))
)
return create_engine(name, con=con, **params)
def convert_time(self, d):
if "time" in d.columns:
if d["time"].dtype == np.dtype("O"):
self.push_code("_d['time'] = pd.to_datetime(_d['time'])")
d["time"] = pd.to_datetime(d["time"])
else:
self.push_code("_d['time'] = pd.to_datetime(_d['time'], unit='s')")
d["time"] = pd.to_datetime(d["time"], unit="s")
def set_index(self, d, index, args):
self.push_code("_d.set_index({}, inplace=True)".format(repr(index)))
d.set_index(index, inplace=True)
if index == "time" and args.timezone:
self.push_code("_d.tz_localize('UTC', copy=False)")
self.push_code("_d.tz_convert('{}', copy=False)".format(args.timezone))
d.tz_localize("UTC", copy=False).tz_convert(args.timezone, copy=False)
def pivot(self, d, args):
def is_dimension(c, t):
return c.endswith("_id") or t == np.dtype("O")
index = d.columns[0]
dimension = [
c for c, t in zip(d.columns[1:], d.dtypes[1:]) if is_dimension(c, t)
]
measure = [
c for c, t in zip(d.columns[1:], d.dtypes[1:]) if not is_dimension(c, t)
]
if len(dimension) == 0:
self.set_index(d, index, args)
return d
if len(dimension) == 1:
dimension = dimension[0]
if len(measure) == 1:
measure = measure[0]
self.push_code(
"_d = _d.pivot({0}, {1}, {2})".format(
repr(index), repr(dimension), repr(measure)
)
)
return d.pivot(index, dimension, measure)
def post_process(self, d, args):
ip = get_ipython()
# convert 'time' to datetime
self.convert_time(d)
# dropna by columns all
if args.dropna:
self.push_code("_d.dropna(axis='columns', how='all', inplace=True)")
d.dropna(axis="columns", how="all", inplace=True)
# pivot_table
if args.pivot:
d = self.pivot(d, args)
elif "time" in d.columns:
self.set_index(d, "time", args)
# return value
r = d
if args.out:
self.push_code("{0} = _d".format(args.out))
ip.push({args.out: d})
r = None
if args.out_file:
if args.out_file[0] in ["'", '"']:
path = os.path.expanduser(ip.ev(args.out_file))
else:
path = os.path.expanduser(args.out_file)
if d.index.name:
self.push_code("_d.to_csv({0})".format(repr(path)))
d.to_csv(path)
else:
self.push_code("_d.to_csv({0}, index=False)".format(repr(path)))
d.to_csv(path, index=False)
print("INFO: saved to '{0}'".format(path))
r = None
if args.plot:
self.push_code("_d.plot()")
r = d.plot()
elif r is not None:
self.push_code("_d")
return r
def run_job(self, line):
ip = get_ipython()
try:
args = self.parse_job_args(line)
except SystemExit:
return
self.code_list = []
self.push_code("# translated code")
if args.connection:
con = ip.ev(args.connection)
else:
con = self.context.connect()
# engine
job = con.get_job(args.job_id)
engine = self.build_engine(job.type, job.database, args)
# read_td_query
self.push_code("_d = pytd.pandas_td.read_td_job({}, _e)".format(args.job_id))
if args.dry_run:
return self.display_code_block()
d = read_td_job(args.job_id, engine)
# output
r = self.post_process(d, args)
if args.verbose:
self.display_code_block()
return r
def run_query(self, engine_type, line, cell):
try:
args = self.parse_query_args(engine_type, line)
except SystemExit:
return
self.code_list = []
self.push_code("# translated code")
query = self.build_query(cell)
engine = self.build_engine(engine_type, args.database, args)
# read_td_query
self.push_code("_d = pytd.pandas_td.read_td_query(_q, _e)")
if args.dry_run:
return self.display_code_block()
d = read_td_query(query, engine)
# output
r = self.post_process(d, args)
if args.verbose:
self.display_code_block()
return r
@magic.line_magic
def td_job(self, line):
"""Get job result.
.. code-block:: python
%td_job [--pivot] [--plot] [--dry-run] [--verbose]
[--connection <connection>] [--dropna] [--out <out>]
[--out-file <out_file>] [--quiet] [--timezone <timezone>]
job_id
Parameters
----------
``<job_id>`` : integer
Job ID.
``--pivot`` : optional
Run pivot_table against dimensions.
``--plot`` : optional
Plot the query result.
``--dry_run``, ``-n`` : optional
Output translated code without running query.
``--verbose``, ``-v`` : optional
Verbose output.
``--connection <connection>``, ``-c <connection>`` : \
pytd.Client, optional
Use specified connection.
``--dropna``, ``-d`` : optional
Drop columns if all values are NA.
``--out <out>``, ``-o <out>`` : string, optional
Store the result to variable.
``--out-file <out_file>``, ``-O <out_file>`` : string, optional
Store the result to file.
``--quiet``, ``-q`` : optional
Disable progress output.
``--timezone <timezone>``, ``-T <timezone>`` : string, optional
Set timezone to time index.
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %td_job 451709460 # select * from sample_datasets.nasdaq limit 5
Out[2]:
symbol open volume high low close
time
1992-08-25 16:00:00 ATRO 0.0 3900 0.7076 0.7076 0.7076
1992-08-25 16:00:00 ALOG 0.0 11200 11.0000 10.6250 11.0000
1992-08-25 16:00:00 ATAX 0.0 11400 11.3750 11.0000 11.0000
1992-08-25 16:00:00 ATRI 0.0 5400 14.3405 14.0070 14.2571
1992-08-25 16:00:00 ABMD 0.0 38800 5.7500 5.2500 5.6875
"""
return self.run_job(line)
@magic.cell_magic
def td_hive(self, line, cell):
"""Run a Hive query.
.. code-block:: python
%%td_hive [<database>] [--pivot] [--plot] [--dry-run] [--verbose]
[--connection <connection>] [--dropna] [--out <out>]
[--out-file <out_file>] [--quiet] [--timezone <timezone>]
<query>
Parameters
----------
``<query>`` : string
Hive query.
``<database>`` : string, optional
Database name.
``--pivot`` : optional
Run pivot_table against dimensions.
``--plot`` : optional
Plot the query result.
``--dry_run``, ``-n`` : optional
Output translated code without running query.
``--verbose``, ``-v`` : optional
Verbose output.
``--connection <connection>``, ``-c <connection>`` : \
pytd.Client, optional
Use specified connection.
``--dropna``, ``-d`` : optional
Drop columns if all values are NA.
``--out <out>``, ``-o <out>`` : string, optional
Store the result to variable.
``--out-file <out_file>``, ``-O <out_file>`` : string, optional
Store the result to file.
``--quiet``, ``-q`` : optional
Disable progress output.
``--timezone <timezone>``, ``-T <timezone>`` : string, optional
Set timezone to time index.
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %%td_hive
...: select hivemall_version()
...:
Out[2]:
_c0
0 0.6.0-SNAPSHOT-201901-r01
"""
return self.run_query("hive", line, cell)
@magic.cell_magic
def td_presto(self, line, cell):
"""Run a Presto query.
.. code-block:: python
%%td_presto [<database>] [--pivot] [--plot] [--dry-run] [--verbose]
[--connection <connection>] [--dropna] [--out <out>]
[--out-file <out_file>] [--quiet] [--timezone <timezone>]
<query>
Parameters
----------
``<query>`` : string
Presto query.
``<database>`` : string, optional
Database name.
``--pivot`` : optional
Run pivot_table against dimensions.
``--plot`` : optional
Plot the query result.
``--dry_run``, ``-n`` : optional
Output translated code without running query.
``--verbose``, ``-v`` : optional
Verbose output.
``--connection <connection>``, ``-c <connection>`` : \
pytd.Client, optional
Use specified connection.
``--dropna``, ``-d`` : optional
Drop columns if all values are NA.
``--out <out>``, ``-o <out>`` : string, optional
Store the result to variable.
``--out-file <out_file>``, ``-O <out_file>`` : string, optional
Store the result to file.
``--quiet``, ``-q`` : optional
Disable progress output.
``--timezone <timezone>``, ``-T <timezone>`` : string, optional
Set timezone to time index.
Returns
-------
:class:`pandas.DataFrame`
Examples
--------
.. code-block:: ipython
In [1]: %load_ext pytd.pandas_td.ipython
In [2]: %%td_presto
...: select * from sample_datasets.nasdaq limit 5
...:
Out[2]:
symbol open volume high low close
time
1989-01-26 16:00:00 SMTC 0.0 8000 0.4532 0.4532 0.4532
1989-01-26 16:00:00 SEIC 0.0 163200 0.7077 0.6921 0.7025
1989-01-26 16:00:00 SIGI 0.0 2800 3.9610 3.8750 3.9610
1989-01-26 16:00:00 NAVG 0.0 1800 14.6740 14.1738 14.6740
1989-01-26 16:00:00 MOCO 0.0 71101 3.6722 3.5609 3.5980
"""
return self.run_query("presto", line, cell)
# extension
def load_ipython_extension(ipython):
ipython.push("get_td_magic_context")
ipython.register_magics(DatabasesMagics)
ipython.register_magics(TablesMagics)
ipython.register_magics(JobsMagics)
ipython.register_magics(UseMagics)
ipython.register_magics(QueryMagics)